import gradio as gr
import requests
from bs4 import BeautifulSoup
import json
import re
from urllib.parse import urljoin, urlparse
import pandas as pd
from io import StringIO

class WebScraper:
    def __init__(self):
        self.session = requests.Session()
        self.session.headers.update({
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36'
        })
    
    def scrape_page(self, url, scrape_type='all'):
        try:
            response = self.session.get(url, timeout=15)
            response.raise_for_status()
            soup = BeautifulSoup(response.text, 'html.parser')
            
            results = {'url': url, 'status': 'success', 'data': {}}
            
            if scrape_type in ['all', 'text']:
                text_content = soup.get_text(separator=' ', strip=True)
                results['data']['text'] = text_content[:10000]
            
            if scrape_type in ['all', 'links']:
                links = []
                for link in soup.find_all('a', href=True):
                    href = link['href']
                    full_url = urljoin(url, href)
                    links.append({'text': link.get_text(strip=True), 'url': full_url})
                results['data']['links'] = links[:50]
            
            if scrape_type in ['all', 'images']:
                images = []
                for img in soup.find_all('img'):
                    src = img.get('src') or img.get('data-src', '')
                    if src:
                        images.append({'src': urljoin(url, src), 'alt': img.get('alt', '')})
                results['data']['images'] = images[:30]
            
            if scrape_type in ['all', 'tables']:
                tables = []
                for table in soup.find_all('table'):
                    rows = []
                    for tr in table.find_all('tr'):
                        cells = [td.get_text(strip=True) for td in tr.find_all(['td', 'th'])]
                        if cells:
                            rows.append(cells)
                    if rows:
                        tables.append(rows)
                results['data']['tables'] = tables[:5]
            
            if scrape_type in ['all', 'headings']:
                headings = {}
                for level in range(1, 7):
                    tags = soup.find_all(f'h{level}')
                    headings[f'h{level}'] = [h.get_text(strip=True) for h in tags]
                results['data']['headings'] = headings
            
            if scrape_type in ['all', 'meta']:
                meta = {}
                for tag in soup.find_all('meta'):
                    name = tag.get('name') or tag.get('property', '')
                    content = tag.get('content', '')
                    if name and content:
                        meta[name] = content
                results['data']['meta'] = meta
            
            return json.dumps(results, indent=2, ensure_ascii=False)
        
        except Exception as e:
            return json.dumps({'url': url, 'status': 'error', 'error': str(e)}, indent=2)
    
    def scrape_multiple(self, urls_text, scrape_type='all'):
        urls = [u.strip() for u in urls_text.split('\n') if u.strip()]
        results = []
        for url in urls[:10]:
            result = self.scrape_page(url, scrape_type)
            results.append(result)
        return '\n\n---\n\n'.join(results)
    
    def export_to_csv(self, url, scrape_type='tables'):
        try:
            response = self.session.get(url, timeout=15)
            soup = BeautifulSoup(response.text, 'html.parser')
            tables = soup.find_all('table')
            if not tables:
                return 'No tables found on this page.'
            
            csv_output = []
            for i, table in enumerate(tables[:3]):
                csv_output.append(f'### Table {i+1}\n')
                df = pd.read_html(StringIO(str(table)))[0]
                csv_output.append(df.to_csv(index=False))
            
            return '\n'.join(csv_output)
        except Exception as e:
            return f'Error: {str(e)}'

def scrape_website(url, scrape_type, multiple_urls):
    scraper = WebScraper()
    if multiple_urls:
        return scraper.scrape_multiple(url, scrape_type)
    return scraper.scrape_page(url, scrape_type)

def export_data(url, scrape_type):
    scraper = WebScraper()
    return scraper.export_to_csv(url, scrape_type)

with gr.Blocks(theme=gr.themes.Soft(), title='Web Scraper Pro') as demo:
    gr.Markdown('# 🕷️ Web Scraper Pro\n\nExtract data from any website with ease. Supports text, links, images, tables, and more.')
    
    with gr.Row():
        with gr.Column(scale=2):
            url_input = gr.Textbox(
                label='Website URL(s)',
                placeholder='https://example.com\n\nFor multiple URLs, enter one per line',
                lines=3
            )
            
            with gr.Row():
                scrape_type = gr.Dropdown(
                    choices=['all', 'text', 'links', 'images', 'tables', 'headings', 'meta'],
                    value='all',
                    label='Scrape Type'
                )
                multiple_urls = gr.Checkbox(label='Multiple URLs Mode', value=False)
            
            scrape_btn = gr.Button('🚀 Start Scraping', variant='primary', size='lg')
            export_btn = gr.Button('📥 Export Tables to CSV', variant='secondary')
        
        with gr.Column(scale=3):
            output = gr.Textbox(
                label='Scraped Results',
                lines=20,
                max_lines=50
            )
    
    gr.Markdown('### 📊 Quick Stats')
    with gr.Row():
        stat_text = gr.Number(label='Characters Extracted', value=0)
        stat_links = gr.Number(label='Links Found', value=0)
        stat_images = gr.Number(label='Images Found', value=0)
    
    gr.Markdown('### 💡 Tips\n- Use **Multiple URLs Mode** to scrape several pages at once\n- Select specific **Scrape Type** for faster results\n- **Export Tables** downloads structured data as CSV')
    
    scrape_btn.click(
        fn=scrape_website,
        inputs=[url_input, scrape_type, multiple_urls],
        outputs=output
    )
    
    export_btn.click(
        fn=export_data,
        inputs=[url_input, scrape_type],
        outputs=output
    )

if __name__ == '__main__':
    demo.launch(server_name='0.0.0.0', server_port=7860, share=True)